



'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A					:       Dict									=     logging.get_logger(__name__)
def 						_a       (    lowerCamelCase_	,		lowerCamelCase_=False	,		lowerCamelCase_=False				):
   snake_case       :				Tuple		    ="""backbone.""" if is_semantic else """"""

   snake_case       :				List[str]		    =[]
   for i in range(config.num_hidden_layers				):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')				)
      rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')				)
      rename_keys.append(
          (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')				)
      rename_keys.append(
          (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')				)
      rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')				)
      rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')				)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')				)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')				)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')				)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')				)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           (F'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
           (F'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
           (F'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
           (F'''{prefix}pos_embed''', '''beit.embeddings.position_embeddings'''),
       ]				)

   if has_lm_head:
      # mask token + layernorm
      rename_keys.extend(
          [
              ('''mask_token''', '''beit.embeddings.mask_token'''),
              ('''norm.weight''', '''layernorm.weight'''),
              ('''norm.bias''', '''layernorm.bias'''),
          ]				)
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
              ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
              ('''head.weight''', '''classifier.weight'''),
              ('''head.bias''', '''classifier.bias'''),
          ]				)

   return rename_keys
def 						_a       (    lowerCamelCase_	,		lowerCamelCase_	,		lowerCamelCase_=False	,		lowerCamelCase_=False				):



   for i in range(config.num_hidden_layers				):
      snake_case       :				Any		    ="""backbone.""" if is_semantic else """"""
      # queries, keys and values
      snake_case       :				Tuple		    =state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''				)
      snake_case       :				Union[str, Any]		    =state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''				)
      snake_case       :				Union[str, Any]		    =state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''				)

      snake_case       :				Optional[int]		    =in_proj_weight[
          : config.hidden_size, :
      ]
      snake_case       :				Dict		    =q_bias
      snake_case       :				List[str]		    =in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      snake_case       :				Optional[Any]		    =in_proj_weight[
          -config.hidden_size :, :
      ]
      snake_case       :				List[Any]		    =v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      snake_case       :				Dict		    =state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''				)
      snake_case       :				Union[str, Any]		    =state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''				)

      snake_case       :				int		    =gamma_a
      snake_case       :				Dict		    =gamma_a
def 						_a       (    lowerCamelCase_	,		lowerCamelCase_	,		lowerCamelCase_				):
   snake_case       :				int		    =dct.pop(a__				)
   snake_case       :				Tuple		    =val
def 						_a       (    ):
   snake_case       :				Union[str, Any]		    ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case       :				Union[str, Any]		    =Image.open(requests.get(a__	,		stream=a__				).raw				)
   return im
@torch.no_grad()
def 						_a       (    lowerCamelCase_	,		lowerCamelCase_	,		lowerCamelCase_=False				):
   snake_case       :				List[Any]		    =False if """rvlcdip""" in checkpoint_url else True
   snake_case       :				Optional[int]		    =BeitConfig(use_absolute_position_embeddings=a__	,		use_mask_token=a__				)

   # size of the architecture
   if "large" in checkpoint_url or "dit-l" in checkpoint_url:
      snake_case       :				int		    =10_24
      snake_case       :				Any		    =40_96
      snake_case       :				str		    =24
      snake_case       :				List[Any]		    =16

   # labels
   if "rvlcdip" in checkpoint_url:
      snake_case       :				Optional[int]		    =16
      snake_case       :				str		    ="""huggingface/label-files"""
      snake_case       :				int		    ="""rvlcdip-id2label.json"""
      snake_case       :				Any		    =json.load(open(hf_hub_download(a__	,		a__	,		repo_type='''dataset'''				)	,		'''r'''				)				)
      snake_case       :				Optional[int]		    ={int(a__				): v for k, v in idalabel.items()}
      snake_case       :				Any		    =idalabel
      snake_case       :				Dict		    ={v: k for k, v in idalabel.items()}

   # load state_dict of original model, remove and rename some keys
   snake_case       :				Union[str, Any]		    =torch.hub.load_state_dict_from_url(a__	,		map_location='''cpu'''				)["""model"""]

   snake_case       :				Dict		    =create_rename_keys(a__	,		has_lm_head=a__				)
   for src, dest in rename_keys:
      rename_key(a__	,		a__	,		a__				)
   read_in_q_k_v(a__	,		a__	,		has_lm_head=a__				)

   # load HuggingFace model
   snake_case       :				Any		    =BeitForMaskedImageModeling(a__				) if has_lm_head else BeitForImageClassification(a__				)
   model.eval()
   model.load_state_dict(a__				)

   # Check outputs on an image
   snake_case       :				Union[str, Any]		    =BeitImageProcessor(
       size=config.image_size	,		resample=PILImageResampling.BILINEAR	,		do_center_crop=a__				)
   snake_case       :				str		    =prepare_img()

   snake_case       :				List[str]		    =image_processor(images=a__	,		return_tensors='''pt'''				)
   snake_case       :				List[str]		    =encoding["""pixel_values"""]

   snake_case       :				List[Any]		    =model(a__				)
   snake_case       :				str		    =outputs.logits

   # verify logits
   snake_case       :				List[str]		    =[1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
   assert logits.shape == torch.Size(a__				), "Shape of logits not as expected"

   Path(a__				).mkdir(exist_ok=a__				)
   print(F'''Saving model to {pytorch_dump_folder_path}'''				)
   model.save_pretrained(a__				)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
   image_processor.save_pretrained(a__				)

   if push_to_hub:
      if has_lm_head:
         snake_case       :				Optional[Any]		    ="""dit-base""" if """base""" in checkpoint_url else """dit-large"""
      else:
         snake_case       :				Tuple		    ="""dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
      image_processor.push_to_hub(
          repo_path_or_name=Path(a__	,		a__				)	,		organization='''nielsr'''	,		commit_message='''Add image processor'''	,		use_temp_dir=a__	,		)
      model.push_to_hub(
          repo_path_or_name=Path(a__	,		a__				)	,		organization='''nielsr'''	,		commit_message='''Add model'''	,		use_temp_dir=a__	,		)


if __name__ == "__main__":
      A					:       Union[str, Any]									=     argparse.ArgumentParser()

      parser.add_argument(
          """--checkpoint_url""",
          default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
          type=str,
          help="""URL to the original PyTorch checkpoint (.pth file).""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
      )
      parser.add_argument(
          """--push_to_hub""",
          action="""store_true""",
      )
      A					:       int									=     parser.parse_args()
      convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return x if y == 0 else greatest_common_divisor(a__  ,	x % y)


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return (x * y) // greatest_common_divisor(a__  ,	a__)


def 					_UpperCAmelCase				(						a__ = 2_0):

			'''simple docstring'''



			a_						:						Any 							= 1
			for i in range(1  ,	n + 1):
						a_						:						Tuple 							= lcm(a__  ,	a__)
			return g


if __name__ == "__main__":
	print(F"""{solution() = }""")


